J. Med. Chem. paper from the Kihlberg and Dobritzsch groups at Uppsala University takes a look at the drugs and candidates beyond Lipinski’s rule of 5. They analyze how such ligands bind to their targets and how different these interactions are from the Ro5 compounds.
First cool thing is that authors use principle component analysis for the analyzed datasets of compounds (I didn’t see it before in the literature). What’s remarkable is that three out of four parameters from the original Ro5 (MW, HBA, and HBD) and some commonly used extensions (PSA and rotatable bonds) actually correlate very well with each other. Which in fact makes perfect sense: the bigger the compound, the more likely it will have more hydrogen bond donors/acceptors and rotatable bonds. cLogP stays a little bit aside probably because it is not cumulative. As a result, the six parameters can be reduced to two principal components accounting for 92% of variance or to three, which cover 97%.
Next, the authors turn their attention to the opposite site of drug discovery business – to the biological targets. It appears that in the relatively new target classes (kinases, proteases, transferases, etc.) non-Ro5 drugs and clinical candidates actually outnumber Ro5-counterparts. Not surprisingly, the parenteral dosing is also prevailing for these compounds (but still 30% are orally available).
Finally, to bring the drugs and targets together, authors analyze binding modes of three identified compound clusters (Ro5, ‘extended’ eRo5 and ‘beyond’ bRo5). As a metric for compound interaction, they use proportion of buried surface, which actually differs a lot between drug clusters. The bigger the deviation from Ro5, the lower the proportion. Seems like bigger molecules don’t need that much coverage by their targets to bind tightly. At the same time, the interface between drugs and targets do not differ regardless of compliance with Ro5. So do affinities measures (IC50, Kd and alike). Authors draw two major conclusions from these observations:
Firstly, drugs outside Ro5 space do not require higher affinities for their targets compared to Ro5 compliant drugs to compensate for any perceived or actual unfavourable pharmacokinetics. Secondly, despite being perceived as “difficult”, binding sites that are larger and more open can be modulated by drugs with similar affinities as drugs directed to sites traditionally considered highly “druggable”.
While the second conclusion is nothing new (it’s not the affinity per se that should be different, it’s how to reach that high affinity that bothers medicinal chemists), the first one casts a shadow on application of metrics such as LE and LLE to the compounds beyond Ro5. LE is predictably lower for bigger molecules and correlates with the compound shapes. Frankly, the latter correlation seems to be redundant as the shape was also well-correlated with compliance to Ro5.
Finally, the authors analyze macrocycles as a representative subclass of bRo5 ligands. Quite counterintuitively they claim that macrocycles are not more rigid than their acyclic bRo5 (pun intended). But that just means that acyclic compounds obtain their rigidity from other sources (e.g. amide and double bonds, aromatic cycles, etc.). In general, from the discussion it follows that there’s nothing too special about macrocycles. The most peculiar feature is their exceptional ability to bind flat protein surfaces. Hence, they are excellent tools for the right problem. So is the rule of five.
In the conclusion, authors propose to extend the boundaries of the original Ro5. Seems like they are too tight. Which raises a logical question, is the next extension just a matter of time?
P.S. Extra kudos to the authors for using R/ggplot2 for graphics in the main text of the paper (I just wonder why they don’t use it in the SI).